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  <section id="module-causalai.models.tabular">
<span id="tabular-base-module"></span><h1>Tabular Base module<a class="headerlink" href="#module-causalai.models.tabular" title="Permalink to this heading"></a></h1>
<section id="causalai-models-tabular-base">
<h2>causalai.models.tabular.base<a class="headerlink" href="#causalai-models-tabular-base" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgo">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">causalai.models.tabular.base.</span></span><span class="sig-name descname"><span class="pre">BaseTabularAlgo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="data.tabular.html#causalai.data.tabular.TabularData" title="causalai.data.tabular.TabularData"><span class="pre">TabularData</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior_knowledge</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="models.common.prior_knowledge.html#causalai.models.common.prior_knowledge.PriorKnowledge" title="causalai.models.common.prior_knowledge.PriorKnowledge"><span class="pre">PriorKnowledge</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgo" title="Permalink to this definition"></a></dt>
<dd><dl class="py method">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgo.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="data.tabular.html#causalai.data.tabular.TabularData" title="causalai.data.tabular.TabularData"><span class="pre">TabularData</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior_knowledge</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="models.common.prior_knowledge.html#causalai.models.common.prior_knowledge.PriorKnowledge" title="causalai.models.common.prior_knowledge.PriorKnowledge"><span class="pre">PriorKnowledge</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgo.__init__" title="Permalink to this definition"></a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>TabularData object</em>) -- It contains data.values, a numpy array of shape (observations N, variables D).</p></li>
<li><p><strong>prior_knowledge</strong> (<em>PriorKnowledge object</em>) -- Specify prior knoweledge to the causal discovery process by either
forbidding links/co-parents that are known to not exist, or adding back links/co-parents that do exist
based on expert knowledge. See the PriorKnowledge class for more details.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgo.get_all_parents">
<span class="sig-name descname"><span class="pre">get_all_parents</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_var</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">List</span></span></span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgo.get_all_parents" title="Permalink to this definition"></a></dt>
<dd><p>Populates the list using all nodes</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgo.get_candidate_mb">
<span class="sig-name descname"><span class="pre">get_candidate_mb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_var</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">List</span></span></span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgo.get_candidate_mb" title="Permalink to this definition"></a></dt>
<dd><p>Populates the list using all the nodes that prior_knowledge allows</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgo.get_candidate_parents">
<span class="sig-name descname"><span class="pre">get_candidate_parents</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_var</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">List</span></span></span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgo.get_candidate_parents" title="Permalink to this definition"></a></dt>
<dd><p>Populates the list using all the nodes that prior_knowledge allows</p>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgo.get_parents">
<span class="sig-name descname"><span class="pre">get_parents</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pvalue_thres</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.05</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgo.get_parents" title="Permalink to this definition"></a></dt>
<dd><p>Assuming run() function has been called for a target_var, get_parents function returns the list of 
parent names that cause the target_var under the given pvalue_thres.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>pvalue_thres</strong> (<em>float</em>) -- Significance level used for hypothesis testing (default: 0.05). 
Candidate parents with pvalues above pvalue_thres are ignored, and the rest are returned as the 
cause of the target_var.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>List of estimated parents.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>list</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgo.run">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_var</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pvalue_thres</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.05</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ResultInfoTabularSingle</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">ResultInfoTabularMB</span></span></span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgo.run" title="Permalink to this definition"></a></dt>
<dd><p>Run causal discovery using the algorithm implemented here</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>target_var</strong> (<em>int</em>) -- Target variable index or name for which parents need to be estimated.</p></li>
<li><p><strong>pvalue_thres</strong> (<em>float</em>) -- Significance level used for hypothesis testing (default: 0.05). 
Candidate parents with pvalues above pvalue_thres are ignored, and the rest are returned as the 
cause of the target_var.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><p>Dictionary has three keys:</p>
<ul class="simple">
<li><p>parents or markov_blanket : List of estimated parents or markov blanket.</p></li>
<li><p>value_dict : Dictionary of form {var3_name:float, ...} containing the test statistic of a link.</p></li>
<li><p>pvalue_dict : Dictionary of form {var3_name:float, ...} containing the p-value corresponding to the above test statistic.</p></li>
</ul>
</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>dict</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgo.sort_parents">
<span class="sig-name descname"><span class="pre">sort_parents</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parents_vals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgo.sort_parents" title="Permalink to this definition"></a></dt>
<dd><p>Sort (in descending order) parents according to test statistic values.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>parents_vals</strong> (<em>dict</em>) -- Dictionary of form {&lt;var_name&gt;:float, ...} containing the test
statistic value of each causal link.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>List of form [&lt;var_i_name&gt;, &lt;var_k_name&gt;, ...] containing sorted parents.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>list</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

<dl class="py class">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgoFull">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">causalai.models.tabular.base.</span></span><span class="sig-name descname"><span class="pre">BaseTabularAlgoFull</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgoFull" title="Permalink to this definition"></a></dt>
<dd><dl class="py method">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgoFull.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgoFull.__init__" title="Permalink to this definition"></a></dt>
<dd></dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgoFull.get_parents">
<span class="sig-name descname"><span class="pre">get_parents</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pvalue_thres</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_var</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgoFull.get_parents" title="Permalink to this definition"></a></dt>
<dd><p>Assuming run() function has been called, get_parents function returns a dictionary. The keys of this
dictionary are the variable names, and the corresponding values are the list of 
parent names that cause the target variable under the given pvalue_thres.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pvalue_thres</strong> (<em>float</em>) -- This pvalue_thres is the significance level used for hypothesis testing (default: 0.05).</p></li>
<li><p><strong>target_var</strong> (<em>str</em><em> or </em><em>float</em><em>, </em><em>optional</em>) -- If specified (must be one of the data variable names), the parents of only this variable
are returned as a list, otherwise a dictionary is returned where each key is a target variable
name, and the corresponding values is the list of its parents.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Dictionary has D keys, where D is the number of variables. The value corresponding each key is
the list of lagged parent names that cause the target variable under the given pvalue_thres.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>dict</p>
</dd>
</dl>
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="causalai.models.tabular.base.BaseTabularAlgoFull.run">
<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pvalue_thres</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.05</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">ResultInfoTabularFull</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#causalai.models.tabular.base.BaseTabularAlgoFull.run" title="Permalink to this definition"></a></dt>
<dd><p>Run causal discovery using the algorithm implemented here for estimating the causal stength of all 
potential parents of all the variables.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>pvalue_thres</strong> (<em>float</em>) -- Significance level used for hypothesis testing (default: 0.05). Candidate parents with pvalues above pvalue_thres
are ignored, and the rest are returned as the cause of the target_var.</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>Dictionay has D keys, where D is the number of variables. The value corresponding each key is 
the dictionary output of BaseTabularAlgo.run.</p>
</dd>
<dt class="field-odd">Return type<span class="colon">:</span></dt>
<dd class="field-odd"><p>dict</p>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>
</section>


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